For years, investing has been all about judgment. The picture was familiar: fund managers spotting trends early, getting information before the street does, flagging risks before markets reacted, and experience & craft guiding decisions. The human edge still matters, no doubt. But in India’s PMS and, for that matter, even in the broader asset-management landscape, decision-making is being greatly aided by technology. Technology is no longer just a support tool. Data, analytics and artificial intelligence are quietly redefining how portfolios are managed.
At its core, Portfolio Management Services is meant to offer customization. Unlike mutual funds, where portfolio construct is highly benchmark driven, PMS options allow portfolios to be tailored to individual entry points & have, most importantly, non-benchmark driven portfolio constructs. Managing multiple non-benchmark-driven portfolios across volatile markets stretches human capacity. The challenge today is less about finding an investible idea &and more about having a process that can generate ideas consistently.
This is where data and analytics have become central to investment decision-making. Indian markets generate an enormous volume of information every day – prices, volumes, earnings, corporate disclosures, macro data, global linkages and capital flows. Modern analytics platforms help investment teams scan this information continuously, flagging unusual behaviour, emerging risks or unintended portfolio concentration. The real value is not prediction, but prioritization. Systems help managers decide what deserves attention, allowing human effort to be spent where it matters most. Managers can run filters that help with a whole bunch of filtration.
AI builds on this foundation by supporting pattern recognition and risk assessment. In practical terms, machine-learning tools are used to rank signals, stress-test portfolios across scenarios, analyse factor exposures and translate unstructured information into usable inputs. Industry research shows that these techniques are increasingly embedded in research and risk workflows, not to replace judgement, but to improve consistency and speed.
Several asset managers now position data science, quantitative frameworks and machine-learning tools as part of their portfolio-management architecture. The rise of quant and factor-based strategies, along with AI-branded funds, signals a broader acceptance of systematic decision frameworks within Indian equity allocation. Even when products are not explicitly marketed as “AI-driven”, the underlying processes increasingly rely on AI models to guide portfolio construction and risk control.
The impact on decision-making shows up in three ways. First, speed with discipline. Data-led systems reduce delays between new information and portfolio action, while enforcing predefined rules on position sizing and risk limits. Second, behavioural control. Markets often exploit human bias – overconfidence, reluctance to exit losing positions, or chasing recent performance. Structured models help introduce consistency at moments when emotion tends to dominate. Third, personalisation at scale. Analytics makes it easier to translate client objectives into portfolio guardrails and explain outcomes through scenario analysis, improving transparency and communication.
None of this suggests the fund manager is becoming irrelevant. Models can fail due to poor data, overfitting or regime shifts. The greater risk is false confidence in back-tested precision. Market conditions change and back-tested models will then fail. In this scenario managers need to modify the formula or language model. That is why human oversight remains essential – especially when interpreting policy changes, geopolitical risks or structural shifts that data alone may not capture.
Regulators are also paying attention. In India, SEBI has required disclosures around AI and machine-learning systems used by mutual funds and has invested heavily in data analytics for market surveillance. Recent consultation papers on responsible AI usage underline the regulator’s intent: innovation is welcome, but accountability cannot be outsourced to algorithms.
Ultimately, technology is not replacing judgment – it is reshaping where judgment is applied. The future of PMS decision-making in India will belong to firms that combine a strong research culture with disciplined, data-driven systems. In that sense, technology may not be the new fund manager, but it is rapidly becoming the backbone of the investment process – and the process is what clients ultimately trust.
Views expressed by: Arun Patel, Founder & Partner at Arunasset Investment Services
Elets The Banking and Finance Post Magazine has carved out a niche for itself in the crowded market with exclusive & unique content. Get in-depth insights on trend-setting innovations & transformation in the BFSI sector. Best offers for Print + Digital issues! Subscribe here➔ www.eletsonline.com/subscription/
